{
 "metadata": {
  "name": "",
  "signature": "sha256:cb588c0980f04c202e45c21516763a8919649f8a97182827b3cbf368112c6db6"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# `prettyplotlib.pcolormesh`: Improving heatmaps in `matplotlib`\n",
      "\n",
      "## Both positive and negative values\n",
      "\n",
      "The default `matplotlib` `pcolormesh` heatmaps use a rainbow colormap, which has been known to mislead data visualization. Specifically, [*\"the rainbow color map is universally inferior to all other color maps\"*](http://www.jwave.vt.edu/~rkriz/Projects/create_color_table/color_07.pdf). Unfortunately, `matplotlib` took its default colors from MATLAB, and there the default is also rainbow."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib\n",
      "# Make sure that original paramters are used, and no matplotlibrc files interfere\n",
      "matplotlib.rcParams = matplotlib.rcParamsDefault"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "#ax.pcolor(np.random.randn((10,10)))\n",
      "#ax.pcolor(np.random.randn(10), np.random.randn(10))\n",
      "p = ax.pcolormesh(np.random.randn(10,10))\n",
      "fig.colorbar(p)\n",
      "fig.savefig('pcolormesh_matplotlib_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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E/OhHP6pQpe8otNfr27dvvvKVr+Tzn/98LrjgglxwwQVpb2/P3//932fVqlX55je/mfr6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\n+Q9kN81mxzsinQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c1ed090>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using the same zero-centered randomly distributed gaussian distribution, we can plot it using `prettplotlib` with a few modifications:\n",
      "\n",
      "    ppl.pcolormesh(fig, ax, np.random.randn(10,10))\n",
      "    \n",
      "You'll notice that the \"hot\" (large, positive) color is still red, and the \"cold\" (small, negative) color is still blue, but the in between colors are gradations of red and blue, so it's easier to tell the difference between values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(fig, ax, np.random.randn(10,10))\n",
      "fig.savefig('pcolormesh_prettyplotlib_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PSVIAAICmIkkBAIAi1Gp7dYfJ3szT6iQpAABAU5GkAABAEfbyDpO9mqfFtf4v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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c4f9c50>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You may have noticed similar changes as were made in `prettyplotlib.scatter`, where axis lines were removed, and blacks were changed to almost black.\n",
      "\n",
      "## Only positive (or negative) values \n",
      "\n",
      "If your data is only positive (or negative), `matplotlib` does nothing to change the color scale. It's still a rainbow, but look at the colorbar, the range is different (0 to 1 instead of -2 to +2)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "p = ax.pcolormesh(np.random.uniform(size=(10,10)))\n",
      "fig.colorbar(p)\n",
      "fig.savefig('pcolormesh_matplotlib_positive_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4AAAAnIPEAwAACtBboT0evTWaHdRm1QAAQE3ReAAAAKWz1AoAAArQnfp0V2Cp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\neBEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c50d8d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If your data is only positive or negative, then `prettyplotlib` will auto-detect this and use a single-color colormap. The default for positive data is the `reds` colormap."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(fig, ax, np.abs(np.random.randn(10,10)))\n",
      "fig.savefig('pcolormesh_prettyplotlib_positive.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pqUler1cxMTEh9yQnJ6umpibkeHd3t3784x9r8uTJmj59uiSpr69P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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c2a8d90>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the default for negative data is the `blues` colormap."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(fig, ax, -np.abs(np.random.randn(10,10)))\n",
      "# fig.savefig('pcolormesh_prettyplotlib_negative.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.collections.QuadMesh at 0x10c518750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RD/T8mtNDcNyfrt3b/y6QpF5R4f/86BjLpq2Z76KPS5cuNXu/e/fuwZ/l76aQ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\nAJ9EMgMAAADAJ/034NfvmQ/ojigAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c242850>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plus you can add $x$- and $y$-ticklabels directly!\n",
      "\n",
      "Normally, when you add $x$- and $y$-ticklables on `pcolormesh` in `matplotlib`, they're not centered on the blocks, and you have to do a lot of annoying work just getting a label on each box. You have to specify the xticks explicitly, since you want to label each box.\n",
      "\n",
      "    xticks = range(10)\n",
      "    yticks = range(10)\n",
      "    \n",
      "    xticklabels=string.uppercase[:10]\n",
      "    yticklabels=string.lowercase[-10:]\n",
      "    \n",
      "    ax.set_xticks(xticks)\n",
      "    ax.set_xticklabels(xticklabels)\n",
      "    \n",
      "    ax.set_yticks(yticks)\n",
      "    ax.set_yticklabels(yticklabels)\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import string\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "p = ax.pcolormesh(np.abs(np.random.randn(10,10)))\n",
      "fig.colorbar(p)\n",
      "\n",
      "xticks = range(10)\n",
      "yticks = range(10)\n",
      "\n",
      "xticklabels=string.uppercase[:10]\n",
      "yticklabels=string.lowercase[-10:]\n",
      "\n",
      "ax.set_xticks(xticks)\n",
      "ax.set_xticklabels(xticklabels)\n",
      "\n",
      "ax.set_yticks(yticks)\n",
      "ax.set_yticklabels(yticklabels)\n",
      "\n",
      "\n",
      "fig.savefig('pcolormesh_matplotlib_positive_labels.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F471SRW/zSdhS0yUYteqTXNMlGPeJZ63pEoy65allpksw7o6Vn5guwSjHRx7TJRh10/+Z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\n2rFjh8aOHStJ8ng8qqysVEpKilJTUzu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o0SJlZmYaSuZaPj4+slqtunHjhukoDdqtW7fU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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c24d490>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But `prettyplotlib.pcolormesh` assumes that you want the `xticklabels` and `yticklabels` on each block, and makes it easy to specify.\n",
      "\n",
      "    ppl.pcolormesh(fig, ax, np.random.uniform(size=(10,10)), \n",
      "                   xticklabels=string.uppercase[:10], \n",
      "                   yticklabels=string.lowercase[-10:])"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import string\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(fig, ax, np.random.randn(10,10), \n",
      "               xticklabels=string.uppercase[:10], \n",
      "               yticklabels=string.lowercase[-10:])\n",
      "fig.savefig('pcolormesh_prettyplotlib_labels.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GJk6cmMcff3zt2KhRozJlypT07ds3V199db75zW/mkEMOye23317a6e0WtQMAQIubPn36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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c26c650>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or pick your own colormap! The diverging colormap `PRGn` or Purple and Green is pretty nice. I usually use this website to look up the colormaps: [Every Colorbrewer Scale](http://bl.ocks.org/mbostock/5577023)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "from prettyplotlib import brewer2mpl\n",
      "import numpy as np\n",
      "import string\n",
      "\n",
      "green_purple = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(fig, ax, np.random.randn(10,10), \n",
      "               xticklabels=string.uppercase[:10], \n",
      "               yticklabels=string.lowercase[-10:],\n",
      "               cmap=green_purple, center_value=0)\n",
      "fig.savefig('pcolormesh_prettyplotlib_labels_other_cmap_diverging.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XtVqtWz49XZfH9js6OvLoo49m7ty5eeCBB6QjAABAp3W5ITn22GPTaDTyd3/3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\nfEB5Tw1LegAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c3d37d0>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "from prettyplotlib import brewer2mpl\n",
      "import numpy as np\n",
      "import string\n",
      "\n",
      "green_purple = brewer2mpl.get_map('PRGn', 'diverging', 11).mpl_colormap\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(fig, ax, np.random.randn(10,10), \n",
      "               xticklabels=string.uppercase[:10], \n",
      "               yticklabels=string.lowercase[-10:],\n",
      "               cmap=green_purple, center_value=2)\n",
      "fig.savefig('pcolormesh_prettyplotlib_labels_other_cmap_diverging_center_value.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zzz8/Z511ViZNmpSWlpYsWLAg3bt3z9e//vV216DRAACAPczixYuTJOvXr89X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\nAACGo2gAAAAAMNx/ABZ4dxW2Pbi3AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c8fe750>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or if you want your own colormap for positive-only data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "from prettyplotlib import brewer2mpl\n",
      "import numpy as np\n",
      "import string\n",
      "\n",
      "red_purple = brewer2mpl.get_map('RdPu', 'Sequential', 9).mpl_colormap\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(fig, ax, np.abs(np.random.randn(10,10)),\n",
      "               xticklabels=string.uppercase[:10], \n",
      "               yticklabels=string.lowercase[-10:],\n",
      "               cmap=red_purple)\n",
      "fig.savefig('pcolormesh_prettyplotlib_labels_other_cmap_sequential.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wrTAcg/BhxACNv2/fPq9zeXl5ysvL8zqfnp6u9PT0fsdMSkrSc88953dtJCQA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\ng9Xz/cZGz+MAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c546310>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plus, this will take the usual parameters of `pcolormesh` like if you want to rescale your data to log-scale:\n",
      "\n",
      "    from matplotlib.colors import LogNorm\n",
      "    ...\n",
      "    ppl.pcolormesh(..., norm=LogNorm(vmin=x.min().min(), vmax=x.max().max()))"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "from prettyplotlib import brewer2mpl\n",
      "import numpy as np\n",
      "import string\n",
      "from matplotlib.colors import LogNorm\n",
      "\n",
      "red_purple = brewer2mpl.get_map('RdPu', 'Sequential', 9).mpl_colormap\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "x = np.abs(np.random.randn(10,10))\n",
      "ppl.pcolormesh(fig, ax, x,\n",
      "               xticklabels=string.uppercase[:10], \n",
      "               yticklabels=string.lowercase[-10:],\n",
      "               cmap=red_purple, \n",
      "               norm=LogNorm(vmin=x.min().min(), vmax=x.max().max()))\n",
      "fig.savefig('pcolormesh_prettyplotlib_labels_lognorm.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AkIRkBAAAcsiyLLKCk4vC16QkJhkBAACSaKhkZIsum6YuIalXZ72RuoTkLnt4ZuoSkurW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\nRgAAgCT+H2tWwRhLSKluAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c253a90>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now you can easily make beautiful heatmaps!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Extras"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import string\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "ppl.pcolormesh(-np.abs(np.random.randn(10,10)),\n",
      "               xticklabels=string.uppercase[:10], \n",
      "               yticklabels=string.lowercase[-10:])\n",
      "fig = plt.gcf()\n",
      "fig.savefig('pcolormesh_prettyplotlib_negative_labels.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wjRGC1yGbfP6lS5daPN+9e3dFRERIkqmmRZJqa2vVrVvT/666du0qSbp+/Xqb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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d4970d0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import string\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "np.random.seed(10)\n",
      "\n",
      "p = ax.pcolormesh(-np.abs(np.random.randn(10,10)))\n",
      "fig.colorbar(p)\n",
      "\n",
      "xticks = range(10)\n",
      "yticks = range(10)\n",
      "\n",
      "xticklabels=string.uppercase[:10]\n",
      "yticklabels=string.lowercase[-10:]\n",
      "\n",
      "ax.set_xticks(xticks)\n",
      "ax.set_xticklabels(xticklabels)\n",
      "\n",
      "ax.set_yticks(yticks)\n",
      "ax.set_yticklabels(yticklabels)\n",
      "\n",
      "fig.savefig('pcolormesh_matplotlib_negative_labels.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L/O+B/3h7qtlTMJXn9B38W0jSzKy/kjTc7Gn0ePn5+R0WBOPGjZMkRUVFqampqd315uZm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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c542750>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}